Representatives of generation ‘Z’ as future doctors – results of research among final year students at medical universities in Poland
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: The nature of the work of doctors is inseparable from responsibility for human health and life, exposure to many risk factors related to physical, chemical, biological and psychosocial risks, as well as the specificity of the organization of the health care system in Poland. This prompted the authors to ask future doctors, currently students of the penultimate and the final year of medical studies, questions about what is important to them in their future profession and how studies at the medical universities met these needs. MATERIAL AND METHODS: Identification of skills important for future doctors to perform their profession was conducted in the third quarter of 2020 in the form of an online diagnostic survey on a sample of 442 fifth- and sixth-year medicine students at medical universities in Poland. RESULTS: The study shows that most students graduating in medicine are satisfied with their choice and intend to work in the profession they have learned. In this study, the responders, on average, felt well prepared theoretically for their future profession, whereas when indicating their practical preparedness, it was much lower. One of the most important skills indicated by students participating in this study was communication with patients. CONCLUSIONS: Overall, the quality of medical studies in Poland is rated very high by the students. Nevertheless, there is a lack of or insufficient time spent on teaching and helping future doctors develop soft skills; therefore, more focus should be placed on this aspect of studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".